6 research outputs found
Recommended from our members
Separating Computational and Statistical Differential Privacy in the Client-Server Model
Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any (possibly adversarial) data analyst is unable to learn too much information that is specific to an individual. Mironov et al. (CRYPTO 2009) proposed several computational relaxations of differential privacy (CDP), which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional client-server model of data analysis. In fact, Groce, Katz and Yerukhimovich (TCC 2011) showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs (zaps) for NP, that there is in fact a computational task in the clientserver model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.Engineering and Applied Science
Practical Two-party Computational Differential Privacy with Active Security.
Distributed models for differential privacy (DP), such as the local and shuffle models, allow for differential privacy without having to trust a single central dataholder. They do however typically require adding more noise than the central model. One commonly iterated remark is that achieving DP with similar accuracy as in the central model is directly achievable by \textit{emulating the trusted party}, using general multiparty computation (MPC), which computes a canonical DP mechanism such as the Laplace or Gaussian mechanism. There have been a few works proposing concrete protocols for doing this but as of yet, all of them either require honest majorities, only allow passive corruptions, only allow computing aggregate functions, lack formal claims of what type of DP is achieved or are not computable in polynomial time by a finite computer. In this work, we propose the first efficiently computable protocol for emulating a dataholder running the geometric mechanism, and which retains its security and DP properties in the presence of dishonest majorities and active corruptions. To this end, we first analyse why current definitions of computational DP are unsuitable for this setting and introduce a new version of computational DP, SIM-CDP. We then demonstrate the merit of this new definition by proving that our protocol satisfies it. Further, we use the protocol to compute two-party inner products with computational DP and with similar levels of accuracy as in the central model, being the first to do so. Finally, we provide an open-sourced implementation of our protocol and benchmark its practical performance
PrivEx: Private collection of traffic statistics for anonymous communication networks
In addition to their common use for private online communication, anonymous communication networks can also be used to circumvent censorship. However, it is difficult to determine the extent to which they are actually used for this purpose without violating the privacy of the networks' users. Knowing this extent can be useful to designers and researchers who would like to improve the performance and privacy properties of the network. To address this issue, we propose a statistical data collection system, PrivEx, for collecting egress traffic statistics from anonymous communication networks in a secure and privacy-preserving manner. Our solution is based on distributed differential privacy and secure multiparty computation; it preserves the security and privacy properties of anonymous communication networks, even in the face of adversaries that can compromise data collection nodes or coerce operators to reveal cryptographic secrets and keys. Copyright is held by the owner/author(s)